Bibliographic record
Abstract
Using the participatory, arts-based information world mapping (IWM) method, this study investigated the information-seeking behaviors of cave divers before, during, and after cave dives. Cave diving is considered an extreme sport, in which technical divers trained in overhead environments penetrate flooded cave systems. 20 participants were interviewed using the IWM technique and semi-structured open-ended questions. Verbatim clean transcriptions and participant maps were coded using thematic content analysis. Participant data suggests cave divers may be intentionally restrictive in their information-sharing behaviors, choosing vetted contacts willing to reciprocate information exchanges. Sport Extrême et Information: Premières Conclusions du Comportement en Recherche d’Information des Plongeurs·euses Spéléologues Professionnel·le·s RésuméEn utilisant la méthode participative de la cartographie du monde de l’information (CMI) basée sur les arts, cette étude a examiné les comportements de recherche d’information des plongeurs·euses avant, pendant, et après la plongée dans les cavernes. La plongée souterraine est considérée comme un sport extrême, où les plongeurs·euses professionnel·le·s formés dans des environnements souterrains évoluent dans des systèmes de grottes inondées. 20 participant·e·s ont été interrogés à l’aide de la technique CMI et de questions ouvertes semi-structurées. Les transcriptions ont été reportées mot pour mot et les cartes des participant·e·s ont été codées au moyen d’une analyse de contenu thématique. Les données des participant·e·s suggèrent que les plongeurs·euses spéléologues peuvent être intentionnellement restrictifs dans leurs comportements de partage d’information, choisissant des contacts approuvés prêts à échanger des informations en retour.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".